Power spectral density and similarity analysis of COVID-19 mortality waves across countries.

Background: During the COVID-19 pandemic, the Johns Hopkins University Center for Systems Science and Engineering (CSSE) established a comprehensive database detailing daily mortality rates across countries. This dataset revealed fluctuating global mortality trends attributable to COVID-19; however, the specific differences and similarities in mortality patterns between countries remain insufficiently explored. Consequently, this study employs Fourier and similarity analyses to examine these patterns within the frequency domain, thereby offering novel insights into the dynamics of COVID-19 mortality waves across different nations. Methods: We employed the Fast Fourier transform to calculate the power spectral density (PSD) of COVID-19 mortality waves in 199 countries from January 22, 2020, to March 9, 2023. Moreover, we performed a cosine similarity analysis of these PSD patterns among all the countries. Results: We identified two dominant peaks in the grand averaged PSD: one at a frequency of 1.15 waves per year (i.e., one wave every 10.4 months) and another at 2.7 waves per year (i.e., one wave every 4.4 months). We also found a cosine similarity index distribution with a skewness of -0.54 and a global median of cosine similarity index of 0.84, thus revealing a remarkable similarity in the dominant peaks of the COVID-19 mortality waves. Conclusion: These findings could be helpful for planetary health if a future pandemic of a similar scale occurs so that effective confinement measures or other actions could be planned during these two identified periods.

Low-field thoracic magnetic stimulation increases peripheral oxygen saturation levels in coronavirus disease (COVID-19) patients: A single-blind, sham-controlled, crossover study.

ABSTRACT: Severe acute respiratory syndrome coronavirus-2 may cause low oxygen saturation (SpO2) and respiratory failure in patients with coronavirus disease (COVID-19). Hence, increased SpO2 levels in COVID-19 patients could be crucial for their quality of life and recovery. This study aimed to demonstrate that a 30-minute single session of dorsal low-field thoracic magnetic stimulation (LF-ThMS) can be employed to increase SpO2 levels in COVID-19 patients significantly. Furthermore, we hypothesized that the variables associated with LF-ThMS, such as frequency, magnetic flux density, and temperature in the dorsal thorax, might be correlated to SpO2 levels in these patients.Here we employed an LF-ThMS device to noninvasively deliver a pulsed magnetic field from 100 to 118 Hz and 10.5 to 13.1 milliTesla (i.e., 105 to 131 Gauss) to the dorsal thorax. These values are within the intensity range of several pulsed electromagnetic field devices employed in physical therapy worldwide. We designed a single-blind, sham-controlled, crossover study on 5 COVID-19 patients who underwent 2 sessions of the study (real and sham LF-ThMS) and 12 patients who underwent only the real LF-ThMS.We found a statistically significant positive correlation between magnetic flux density, frequency, or temperature, associated with the real LF-ThMS and SpO2 levels in all COVID-19 patients. However, the 5 patients in the sham-controlled study did not exhibit a significant change in their SpO2 levels during sham stimulation. The employed frequencies and magnetic flux densities were safe for the patients. We did not observe adverse events after the LF-ThMS intervention.This study is a proof-of-concept that a single session of LF-ThMS applied for 30 minutes to the dorsal thorax of 17 COVID-19 patients significantly increased their SpO2 levels. However, future research will be needed to understand the physiological mechanisms behind this finding.The study was registered at ClinicalTrials.gov (Identifier: NCT04895267, registered on May 20, 2021) retrospectively registered. https://clinicaltrials.gov/ct2/show/NCT04895267.

Modeling Post-Scratching Locomotion with Two Rhythm Generators and a Shared Pattern Formation.

This study aimed to present a model of post-scratching locomotion with two intermixed central pattern generator (CPG) networks, one for scratching and another for locomotion. We hypothesized that the rhythm generator layers for each CPG are different, with the condition that both CPGs share their supraspinal circuits and their motor outputs at the level of their pattern formation networks. We show that the model reproduces the post-scratching locomotion latency of 6.2 ± 3.5 s, and the mean cycle durations for scratching and post-scratching locomotion of 0.3 ± 0.09 s and 1.7 ± 0.6 s, respectively, which were observed in a previous experimental study. Our findings show how the transition of two rhythmic movements could be mediated by information exchanged between their CPG circuits through routes converging in a common pattern formation layer. This integrated organization may provide flexible and effective connectivity despite the rigidity of the anatomical connections in the spinal cord circuitry.

Improved detection of magnetic signals by a MEMS sensor using stochastic resonance.

We introduce the behavior of the electrical output response of a magnetic field sensor based on microelectromechanical systems (MEMS) technology under different levels of controlled magnetic noise. We explored whether a particular level of magnetic noise applied on the vicinity of the MEMS sensor can improve the detection of subthreshold magnetic fields. We examined the increase in the signal-to-noise ratio (SNR) of such detected magnetic fields as a function of the magnetic noise intensity. The data disclosed an inverted U-like graph between the SNR and the applied magnetic noise. This finding shows that the application of an intermediate level of noise in the environment of a MEMS magnetic field sensor improves its detection capability of subthreshold signals via the stochastic resonance phenomenon.

Digital signal processing by virtual instrumentation of a MEMS magnetic field sensor for biomedical applications.

We present a signal processing system with virtual instrumentation of a MEMS sensor to detect magnetic flux density for biomedical applications. This system consists of a magnetic field sensor, electronic components implemented on a printed circuit board (PCB), a data acquisition (DAQ) card, and a virtual instrument. It allows the development of a semi-portable prototype with the capacity to filter small electromagnetic interference signals through digital signal processing. The virtual instrument includes an algorithm to implement different configurations of infinite impulse response (IIR) filters. The PCB contains a precision instrumentation amplifier, a demodulator, a low-pass filter (LPF) and a buffer with operational amplifier. The proposed prototype is used for real-time non-invasive monitoring of magnetic flux density in the thoracic cage of rats. The response of the rat respiratory magnetogram displays a similar behavior as the rat electromyogram (EMG).

Respiratory magnetogram detected with a MEMS device.

Magnetic fields generated by the brain or the heart are very useful in clinical diagnostics. Therefore, magnetic signals produced by other organs are also of considerable interest. Here we show first evidence that thoracic muscles can produce a strong magnetic flux density during respiratory activity, that we name respiratory magnetogram. We used a small magnetometer based on microelectromechanical systems (MEMS), which was positioned inside the open thoracic cage of anaesthetized and ventilated rats. With this new MEMS sensor of about 20 nT resolution, we recorded a strong and rhythmic respiratory magnetogram of about 600 nT.